Fusion of texture features and SBS method for classification of tobacco leaves for automatic harvesting

Mallikarjuna, P. B. and Guru, D. S. (2013) Fusion of texture features and SBS method for classification of tobacco leaves for automatic harvesting. In: Multimedia Processing, Communication and Computing Applications. Springer, New Delhi, pp. 115-126. ISBN 978-81-322-1143-3

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Official URL: http://doi.org/10.1007/978-81-322-1143-3_10

Abstract

In this paper we propose a new model to classify tobacco leaves for automatic harvesting using feature level fusion. The CIELAB color space model is used to segment leaves from their background. Texture features are extracted from segmented leaves using Haar wavelets and gray level local texture pattern (GLTP) separately. These extracted features are fused using the concatenation rule. Discriminative texture features are then selected using the sequential backward selection (SBS) method. The k-NN classifier is designed to classify tobacco leaves into three classes viz., unripe, ripe and over-ripe. In order to corroborate the efficacy of the proposed model, we have conducted an experimentation on our own dataset consisting of 1,300 images of tobacco leaves captured in sunny and cloudy lighting conditions in a real tobacco field.

Item Type: Book Section
Uncontrolled Keywords: CIELAB color, Communication, Feature level fusion, k-NN classifier, Local Texture, Sequential backward selection, Textures, Tobacco, Tobacco leave, Wavelets
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: Arshiya Kousar
Date Deposited: 21 Oct 2019 06:34
Last Modified: 21 Oct 2019 06:34
URI: http://eprints.uni-mysore.ac.in/id/eprint/9144

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